A Bayesian framework for estimating disease risk due to exposure to uranium mine and mill waste on the Navajo Nation
A Bayesian framework for estimating disease risk due to exposure to uranium mine and mill waste on the Navajo Nation
复制标题
估算纳瓦霍部落因接触铀矿和工厂废物而患病风险的贝叶斯框架
DOI：
10.1111/rssa.12099
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发表时间：
2015-10-01
影响因子：
2
通讯作者：
Lewis, Johnnye
中科院分区：
文献类型：
--
作者：
Hund, Lauren;Bedrick, Edward J.;Lewis, Johnnye
超过1100个废弃的矿山、磨矿场和铀矿开采时期的废料堆散落在纳瓦霍族各地，导致他们暴露在包括铀在内的环境金属中。环境卫生网络项目的启动是为了回应对这些环境暴露对慢性病的社区健康影响的关注。这篇论文介绍了环境健康Dine网络对居住在纳瓦霍族的1304个人的初步调查结果。我们研究了铀矿废物暴露与肾脏疾病、糖尿病和高血压的关系。这些慢性疾病在研究人群中发病率很高，构成重大公共卫生风险，并在其他研究中与金属接触有关。我们通过使用三种二元反应的多变量模型来模拟暴露-结果关系。我们实现了一个贝叶斯多元t模型，它具有边际对数-比值比参数解释，并且计算效率高。在检查环境暴露时，适当调整潜在的混杂因素对于获得与政策相关的影响估计至关重要。我们使用贝叶斯模型平均来解释在一小组测量的混杂因素中混淆调整的函数形式的不确定性。使用这个多变量框架，我们发现这些慢性疾病与历史采矿时代和遗留采矿暴露之间存在关联的证据。
More than 1100 abandoned mines, milling sites and waste piles from the uranium mining period are scattered across the Navajo Nation, resulting in exposures to environmental metals, including uranium. The Dine Network for Environmental Health project began in response to concerns regarding the community health effects of these environmental exposures on chronic disease. The paper presents the results of the initial Dine Network for Environmental Health survey of 1304 individuals living on the Navajo Nation. We examine the relationship between uranium mine waste exposure and kidney disease, diabetes and hypertension. These chronic diseases are found at high prevalences in the study population, present major public health risks and have been linked to metals exposures in other studies. We model the exposure-outcome relationship by using a multivariate model for the three binary responses. We implement a Bayesian multivariate t-model, which has marginal log-odds ratio parameter interpretations and is computationally efficient. In examining environmental exposures, appropriately adjusting for potential confounders is pivotal to obtaining policy relevant effect estimates. We use Bayesian model averaging to account for uncertainty in the functional form for confounding adjustment within a small set of measured confounders. Using this multivariate framework, we find evidence of associations between these chronic diseases and both historic mining era and legacy mining exposures.